"""
SIMPLE求解器模块

本模块实现了基于SIMPLE算法的双流体模型求解器，包括：
1. Picard迭代
2. Krylov子空间方法
3. 压力修正方程求解

理论基础：
- SIMPLE算法
- Picard迭代法
- GMRES方法
- 预处理技术

作者：[您的名字]
日期：[创建日期]
"""

import numpy as np
from typing import Dict, Tuple, List
from scipy.sparse import csr_matrix
from scipy.sparse.linalg import gmres
from dataclasses import dataclass
from ..physics.two_fluid_model import FluidState
from .discretization import Discretization, StaggeredGrid
from ..utils.logger import get_logger

@dataclass
class SolverConfig:
    """求解器配置数据类"""
    max_iterations: int  # 最大迭代次数
    tolerance: float  # 收敛容差
    relaxation_factor: float  # 松弛因子
    min_time_step: float  # 最小时间步长
    max_time_step: float  # 最大时间步长

class SimpleSolver:
    """
    基于SIMPLE算法的双流体模型求解器
    
    实现了双流体模型的数值求解，包括：
    - Picard迭代求解非线性方程组
    - GMRES求解线性方程
    - 压力修正方程的构建和求解
    """
    
    def __init__(
        self,
        grid: StaggeredGrid,
        discretization: Discretization,
        config: dict
    ):
        """
        初始化求解器
        
        参数:
            grid: StaggeredGrid, 交错网格对象
            discretization: Discretization, 离散化对象
            config: dict, 配置字典
        """
        self.logger = get_logger()
        self.grid = grid
        self.discretization = discretization
        
        # 求解器配置
        self.config = SolverConfig(
            max_iterations=config['solver']['max_iterations'],
            tolerance=config['solver']['tolerance'],
            relaxation_factor=config['solver']['relaxation_factor'],
            min_time_step=config['solver']['min_time_step'],
            max_time_step=config['solver']['max_time_step']
        )
        
        # 当前时间步长
        self.dt = config['numerical']['time_step']
        
        # 初始化求解器状态
        self._initialize_solver()
    
    def _initialize_solver(self):
        """初始化求解器状态"""
        # 初始化矩阵和向量
        n_cells = len(self.grid.scalar_cells)
        self.A = np.zeros((4*n_cells, 4*n_cells))  # 系数矩阵
        self.b = np.zeros(4*n_cells)  # 右端项向量
        
        # 初始化预处理器
        self._initialize_preconditioner()
    
    def _initialize_preconditioner(self):
        """初始化预处理器"""
        # 基于物理的预处理器，仅考虑轴向相邻单元
        n_cells = len(self.grid.scalar_cells)
        self.M = np.zeros((4*n_cells, 4*n_cells))
        
        # TODO: 实现基于物理的预处理器矩阵构建
    
    def solve_time_step(
        self,
        current_state: Dict[str, FluidState],
        prev_state: Dict[str, FluidState],
        boundary_conditions: dict
    ) -> Tuple[Dict[str, FluidState], bool]:
        """
        求解一个时间步
        
        参数:
            current_state: 当前时刻的流体状态
            prev_state: 上一时刻的流体状态
            boundary_conditions: 边界条件
            
        返回:
            Tuple[Dict[str, FluidState], bool]: (新的流体状态, 是否收敛)
        """
        # Picard迭代
        for iter in range(self.config.max_iterations):
            # 1. 计算热通量
            q_wk = self._calculate_heat_flux(current_state, boundary_conditions)
            
            # 2. 求解动量方程获得质量流量
            mass_flux = self._solve_momentum_equations(
                current_state,
                prev_state,
                boundary_conditions
            )
            
            # 3. 使用PGMRES求解质量-能量方程
            delta_state = self._solve_mass_energy_equations(
                current_state,
                prev_state,
                mass_flux,
                q_wk,
                boundary_conditions
            )
            
            # 4. 更新物理量
            new_state = self._update_state(current_state, delta_state)
            
            # 5. 计算残差并检查收敛性
            residual = self._calculate_residual(new_state, current_state)
            if residual < self.config.tolerance:
                return new_state, True
            
            # 更新当前状态
            current_state = new_state
        
        # 如果未收敛，减小时间步长重试
        if self.dt > self.config.min_time_step:
            self.dt *= 0.5
            return self.solve_time_step(current_state, prev_state, boundary_conditions)
        else:
            self.logger.error("求解未收敛")
            return current_state, False
    
    def _solve_momentum_equations(
        self,
        current_state: Dict[str, FluidState],
        prev_state: Dict[str, FluidState],
        boundary_conditions: dict
    ) -> Dict[str, np.ndarray]:
        """
        求解动量方程
        
        参数:
            current_state: 当前时刻的流体状态
            prev_state: 上一时刻的流体状态
            boundary_conditions: 边界条件
            
        返回:
            Dict[str, np.ndarray]: 各相的质量流量
        """
        mass_flux = {}
        
        for phase in ['liquid', 'vapor']:
            # 轴向动量方程
            axial_momentum = np.zeros(len(self.grid.velocity_cells_x))
            for i in range(len(self.grid.velocity_cells_x)):
                axial_momentum[i] = self.discretization.discretize_axial_momentum(
                    current_state[phase],
                    prev_state[phase],
                    current_state['pressure'],
                    current_state[f'{phase}_wall_friction'],
                    current_state[f'{phase}_interface_friction'],
                    current_state[f'{phase}_turbulent_mixing'],
                    i
                )
            
            # 横向动量方程
            lateral_momentum = np.zeros(len(self.grid.velocity_cells_y))
            for i in range(len(self.grid.velocity_cells_y)):
                lateral_momentum[i] = self.discretization.discretize_lateral_momentum(
                    current_state[phase],
                    prev_state[phase],
                    current_state['pressure'],
                    current_state[f'{phase}_wall_friction'],
                    current_state[f'{phase}_interface_friction'],
                    current_state[f'{phase}_turbulent_mixing'],
                    i
                )
            
            # 计算质量流量
            mass_flux[phase] = {
                'axial': axial_momentum * current_state[phase].rho,
                'lateral': lateral_momentum * current_state[phase].rho
            }
        
        return mass_flux
    
    def _solve_mass_energy_equations(
        self,
        current_state: Dict[str, FluidState],
        prev_state: Dict[str, FluidState],
        mass_flux: Dict[str, np.ndarray],
        q_wk: Dict[str, np.ndarray],
        boundary_conditions: dict
    ) -> Dict[str, np.ndarray]:
        """
        求解质量-能量方程组
        
        参数:
            current_state: 当前时刻的流体状态
            prev_state: 上一时刻的流体状态
            mass_flux: 质量流量
            q_wk: 壁面热通量
            boundary_conditions: 边界条件
            
        返回:
            Dict[str, np.ndarray]: 状态变量的增量
        """
        # 构建质量-能量方程组的系数矩阵和右端项
        self._build_mass_energy_matrix(
            current_state,
            prev_state,
            mass_flux,
            q_wk,
            boundary_conditions
        )
        
        # 使用PGMRES求解线性方程组
        x, info = gmres(
            csr_matrix(self.A),
            self.b,
            M=csr_matrix(self.M),
            tol=self.config.tolerance
        )
        
        if info != 0:
            self.logger.warning(f"GMRES求解未收敛，info={info}")
        
        # 将解向量转换为状态变量增量
        delta_state = self._convert_solution_to_state(x)
        
        return delta_state
    
    def _build_mass_energy_matrix(
        self,
        current_state: Dict[str, FluidState],
        prev_state: Dict[str, FluidState],
        mass_flux: Dict[str, np.ndarray],
        q_wk: Dict[str, np.ndarray],
        boundary_conditions: dict
    ):
        """构建质量-能量方程组的系数矩阵和右端项"""
        n_cells = len(self.grid.scalar_cells)
        
        # 重置矩阵和向量
        self.A.fill(0.0)
        self.b.fill(0.0)
        
        for i in range(n_cells):
            # 液相质量守恒
            row = 4*i
            self._build_mass_conservation_row(
                row, i, 'liquid',
                current_state, prev_state,
                mass_flux, boundary_conditions
            )
            
            # 气相质量守恒
            row = 4*i + 1
            self._build_mass_conservation_row(
                row, i, 'vapor',
                current_state, prev_state,
                mass_flux, boundary_conditions
            )
            
            # 液相能量守恒
            row = 4*i + 2
            self._build_energy_conservation_row(
                row, i, 'liquid',
                current_state, prev_state,
                mass_flux, q_wk, boundary_conditions
            )
            
            # 气相能量守恒
            row = 4*i + 3
            self._build_energy_conservation_row(
                row, i, 'vapor',
                current_state, prev_state,
                mass_flux, q_wk, boundary_conditions
            )
    
    def _build_mass_conservation_row(
        self,
        row: int,
        cell_idx: int,
        phase: str,
        current_state: Dict[str, FluidState],
        prev_state: Dict[str, FluidState],
        mass_flux: Dict[str, np.ndarray],
        boundary_conditions: dict
    ):
        """构建质量守恒方程的矩阵行"""
        cell = self.grid.scalar_cells[cell_idx]
        channel_type = boundary_conditions['channel_types'][cell_idx]
        
        # 获取相邻通道
        neighbors = boundary_conditions['channel_connections'][cell_idx]
        
        # 时间导数项系数
        dt_coeff = cell.volume / self.dt
        self.A[row, row] += dt_coeff
        
        # 对流项系数
        for neighbor in neighbors:
            # 获取连接宽度
            gap_width = boundary_conditions['connection_gaps'].get((cell_idx, neighbor))
            if gap_width is None:
                continue
                
            # 计算横向流动面积
            if channel_type == 'corner':
                flow_area = 0.7 * gap_width * cell.dz  # 角通道流动系数0.7
            elif channel_type == 'edge':
                flow_area = 0.9 * gap_width * cell.dz  # 边通道流动系数0.9
            else:  # center
                flow_area = gap_width * cell.dz  # 中心通道流动系数1.0
            
            # 添加横向流动项
            if phase == 'liquid':
                velocity = current_state[phase].v
            else:
                velocity = current_state[phase].v
                
            flux = current_state[phase].alpha * current_state[phase].rho * velocity * flow_area
            
            # 添加到矩阵
            self.A[row, 4*neighbor] += flux  # alpha项
            self.A[row, 4*neighbor+1] += flux * current_state[phase].h  # 焓项
        
        # ���（相变）
        if phase == 'liquid':
            self.b[row] -= boundary_conditions['gamma'] * cell.volume
        else:
            self.b[row] += boundary_conditions['gamma'] * cell.volume

    def _build_energy_conservation_row(
        self,
        row: int,
        cell_idx: int,
        phase: str,
        current_state: Dict[str, FluidState],
        prev_state: Dict[str, FluidState],
        mass_flux: Dict[str, np.ndarray],
        q_wk: Dict[str, np.ndarray],
        boundary_conditions: dict
    ):
        """构建能量守恒方程的矩阵行"""
        cell = self.grid.scalar_cells[cell_idx]
        channel_type = boundary_conditions['channel_types'][cell_idx]
        
        # 获取通道几何参数
        geometry = boundary_conditions['channel_geometry'][channel_type]
        heated_perimeter = geometry['heated_perimeter']
        
        # 时间导数项系数
        dt_coeff = cell.volume / self.dt
        self.A[row, row] += dt_coeff
        
        # 对流项系数（与质量守恒方程类似）
        neighbors = boundary_conditions['channel_connections'][cell_idx]
        for neighbor in neighbors:
            gap_width = boundary_conditions['connection_gaps'].get((cell_idx, neighbor))
            if gap_width is None:
                continue
                
            # 计算横向流动面积
            if channel_type == 'corner':
                flow_area = 0.7 * gap_width * cell.dz
            elif channel_type == 'edge':
                flow_area = 0.9 * gap_width * cell.dz
            else:  # center
                flow_area = gap_width * cell.dz
            
            # 添加横向能量输运项
            if phase == 'liquid':
                velocity = current_state[phase].v
            else:
                velocity = current_state[phase].v
                
            flux = current_state[phase].alpha * current_state[phase].rho * \
                   current_state[phase].h * velocity * flow_area
            
            # 添加到矩阵
            self.A[row, 4*neighbor] += flux
            self.A[row, 4*neighbor+1] += flux
        
        # 壁面传热项
        if phase == 'liquid':
            # 液相壁面传热
            q_wall = q_wk['liquid'][cell_idx]
            self.b[row] -= q_wall * heated_perimeter * cell.dz
        else:
            # 气相壁面传热
            q_wall = q_wk['vapor'][cell_idx]
            self.b[row] -= q_wall * heated_perimeter * cell.dz
        
        # 相变潜热
        if phase == 'liquid':
            self.b[row] -= boundary_conditions['gamma'] * \
                          current_state[phase].h * cell.volume
        else:
            self.b[row] += boundary_conditions['gamma'] * \
                          current_state[phase].h * cell.volume
        
        # 压力功
        if phase == 'liquid':
            self.b[row] -= current_state[phase].alpha * \
                          (current_state['pressure'] - prev_state['pressure']) * \
                          cell.volume / self.dt
        else:
            self.b[row] -= current_state[phase].alpha * \
                          (current_state['pressure'] - prev_state['pressure']) * \
                          cell.volume / self.dt
    
    def _convert_solution_to_state(
        self,
        x: np.ndarray
    ) -> Dict[str, np.ndarray]:
        """
        将求解得到的向量转换为状态变量增量
        
        参数:
            x: np.ndarray, 求解得到的向量
            
        返回:
            Dict[str, np.ndarray]: 状态变量的增量
        """
        n_cells = len(self.grid.scalar_cells)
        
        delta_state = {
            'liquid': {
                'alpha': x[0::4][:n_cells],
                'h': x[1::4][:n_cells]
            },
            'vapor': {
                'alpha': x[2::4][:n_cells],
                'h': x[3::4][:n_cells]
            },
            'pressure': x[4::4][:n_cells]
        }
        
        return delta_state
    
    def _update_state(
        self,
        current_state: Dict[str, FluidState],
        delta_state: Dict[str, np.ndarray]
    ) -> Dict[str, FluidState]:
        """
        更新流体状态
        
        参数:
            current_state: 当前流体状态
            delta_state: 状态变量的增量
            
        返回:
            Dict[str, FluidState]: 更新后的流体状态
        """
        new_state = {}
        
        for phase in ['liquid', 'vapor']:
            # 使用松弛因子更新状态变量
            alpha = current_state[phase].alpha + \
                   self.config.relaxation_factor * delta_state[phase]['alpha']
            h = current_state[phase].h + \
                self.config.relaxation_factor * delta_state[phase]['h']
            
            # 确保物理约束
            alpha = np.clip(alpha, 0.0, 1.0)
            
            # 更新状态
            new_state[phase] = FluidState(
                alpha=alpha,
                h=h,
                rho=current_state[phase].rho,  # 需要通过物性计算更新
                u=current_state[phase].u,
                v=current_state[phase].v,
                w=current_state[phase].w,
                T=current_state[phase].T,  # 需要通过物性计算更新
                mu=current_state[phase].mu,
                k=current_state[phase].k,
                cp=current_state[phase].cp
            )
        
        # 更新压力
        new_state['pressure'] = current_state['pressure'] + \
                              self.config.relaxation_factor * delta_state['pressure']
        
        return new_state
    
    def _calculate_residual(
        self,
        new_state: Dict[str, FluidState],
        current_state: Dict[str, FluidState]
    ) -> float:
        """
        计算残差
        
        参数:
            new_state: 新的流体状态
            current_state: 当前流体状态
            
        返回:
            float: 残差
        """
        residual = 0.0
        
        for phase in ['liquid', 'vapor']:
            # 计算状态变量的相对变化
            d_alpha = np.abs(new_state[phase].alpha - current_state[phase].alpha) / \
                     (np.abs(current_state[phase].alpha) + 1e-10)
            d_h = np.abs(new_state[phase].h - current_state[phase].h) / \
                  (np.abs(current_state[phase].h) + 1e-10)
            
            residual = max(residual, np.max(d_alpha), np.max(d_h))
        
        # 压力残差
        d_p = np.abs(new_state['pressure'] - current_state['pressure']) / \
              (np.abs(current_state['pressure']) + 1e-10)
        residual = max(residual, np.max(d_p))
        
        return residual
    
    def _calculate_heat_flux(
        self,
        current_state: Dict[str, FluidState],
        boundary_conditions: dict
    ) -> Dict[str, np.ndarray]:
        """
        计算壁面热通量
        
        参数:
            current_state: 当前流体状态
            boundary_conditions: 边界条件
            
        返回:
            Dict[str, np.ndarray]: 各相的壁面热通量
        """
        q_wl = np.zeros((self.n_axial, self.n_channels))
        q_wv = np.zeros((self.n_axial, self.n_channels))
        
        # 遍历所有网格点计算热通量
        for i in range(self.n_axial):
            for j in range(self.n_channels):
                q_wl[i,j], q_wv[i,j] = self.calculate_wall_heat_flux(j, i)
                
        return {
            'liquid': q_wl,
            'vapor': q_wv
        }

    def calculate_wall_heat_flux(self, channel_idx, axial_idx):
        """
        计算壁面热通量
        
        参数:
            channel_idx: 子通道索引
            axial_idx: 轴向位置索引
            
        返回:
            q_wl: 液相壁面热通量
            q_wv: 气相壁面热通量
        """
        # 获取通道类型和几何参数
        channel_type = self.channel_types[channel_idx]
        heated_perimeter = self.channel_geometry[channel_type]['heated_perimeter']
        
        # 获取当前状态
        p = self.pressure[axial_idx, channel_idx]
        T_l = self.T_l[axial_idx, channel_idx]
        T_v = self.T_v[axial_idx, channel_idx]
        alpha = self.void_fraction[axial_idx, channel_idx]
        u_l = self.liquid_velocity[axial_idx, channel_idx]
        u_v = self.vapor_velocity[axial_idx, channel_idx]
        
        # 获取流体物性
        liquid_props = self.fluid_properties.get_properties(p, T_l, 'liquid')
        vapor_props = self.fluid_properties.get_properties(p, T_v, 'vapor')
        
        # 计算壁温（这里假设给定）
        T_wall = self.wall_temperature[axial_idx, channel_idx]
        
        # 计算单相对流换热系数（Dittus-Boelter相关式）
        Re_l = liquid_props['density'] * abs(u_l) * self.hydraulic_diameter / liquid_props['viscosity']
        Pr_l = liquid_props['specific_heat'] * liquid_props['viscosity'] / liquid_props['thermal_conductivity']
        
        if Re_l > 2300:  # 湍流
            Nu_l = 0.023 * Re_l**0.8 * Pr_l**0.4
        else:  # 层流
            Nu_l = 4.36
            
        h_l = Nu_l * liquid_props['thermal_conductivity'] / self.hydraulic_diameter
        
        # 如果是两相流，使用Chen相关式
        if alpha > 0:
            # 计算气相对流换热系数
            Re_v = vapor_props['density'] * abs(u_v) * self.hydraulic_diameter / vapor_props['viscosity']
            Pr_v = vapor_props['specific_heat'] * vapor_props['viscosity'] / vapor_props['thermal_conductivity']
            
            if Re_v > 2300:
                Nu_v = 0.023 * Re_v**0.8 * Pr_v**0.4
            else:
                Nu_v = 4.36
                
            h_v = Nu_v * vapor_props['thermal_conductivity'] / self.hydraulic_diameter
            
            # 计算两相流增强系数
            F = 1.0 + 2.4e4 * (alpha/(1-alpha))**0.8
            
            # 修正换热系数
            h_l *= F
            h_v *= F
        
        # 计算热通量
        q_wl = h_l * (T_wall - T_l) * (1 - alpha)
        q_wv = h_v * (T_wall - T_v) * alpha if alpha > 0 else 0.0
        
        return q_wl, q_wv

    def build_preconditioner(self):
        """构建基于物理的预处理器矩阵"""
        n = self.n_channels * self.n_axial * 4  # 总自由度数（每个节点4个变量）
        M = sp.lil_matrix((n, n))
        
        # 遍历所有网格点
        for i in range(self.n_axial):
            for j in range(self.n_channels):
                idx = self.get_matrix_index(i, j)
                
                # 获取当前状态
                p = self.pressure[i, j]
                alpha = self.void_fraction[i, j]
                rho_l = self.liquid_density[i, j]
                rho_v = self.vapor_density[i, j]
                u_l = self.liquid_velocity[i, j]
                u_v = self.vapor_velocity[i, j]
                
                # 1. 质量守恒方程的对角元
                M[idx, idx] = max(abs(rho_l * (1-alpha)), abs(rho_v * alpha))
                
                # 2. 动量守恒方程的对角元
                # 液相动量方程
                M[idx+1, idx+1] = max(
                    abs(rho_l * (1-alpha) * u_l),  # 惯性项
                    abs(self.interfacial_friction_coefficient),  # 界面摩擦项
                    abs(self.wall_friction_coefficient_l)  # 壁面摩擦项
                )
                
                # 气相动量方程
                M[idx+2, idx+2] = max(
                    abs(rho_v * alpha * u_v),  # 惯性项
                    abs(self.interfacial_friction_coefficient),  # 界面摩擦项
                    abs(self.wall_friction_coefficient_v)  # 壁面摩擦项
                )
                
                # 3. 能量守恒方程的对角元
                M[idx+3, idx+3] = max(
                    abs(rho_l * (1-alpha) * self.liquid_specific_heat),  # 液相热容
                    abs(rho_v * alpha * self.vapor_specific_heat)  # 气相热容
                )
                
                # 4. 考虑相邻节点的耦合
                if i > 0:  # 上游节点
                    up_idx = self.get_matrix_index(i-1, j)
                    M[idx:idx+4, up_idx:up_idx+4] = 0.1 * M[idx:idx+4, idx:idx+4]
                    
                if i < self.n_axial-1:  # 下游节点
                    down_idx = self.get_matrix_index(i+1, j)
                    M[idx:idx+4, down_idx:down_idx+4] = 0.1 * M[idx:idx+4, idx:idx+4]
                    
                # 5. 考虑横向连接的子通道
                for connected_channel in self.channel_connections[j]:
                    conn_idx = self.get_matrix_index(i, connected_channel)
                    M[idx:idx+4, conn_idx:conn_idx+4] = 0.05 * M[idx:idx+4, idx:idx+4]
        
        # 转换为CSR格式以提高计算效率
        return M.tocsr()
        
    def get_matrix_index(self, axial_idx, channel_idx):
        """获取矩阵中的索引位置"""
        return (axial_idx * self.n_channels + channel_idx) * 4